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코드 샘플
Java
이 샘플을 사용해 보기 전에 Vision 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vision Java API 참조 문서를 참조하세요.
Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import com.google.cloud.vision.v1.AnnotateImageRequest;
import com.google.cloud.vision.v1.AsyncBatchAnnotateImagesRequest;
import com.google.cloud.vision.v1.AsyncBatchAnnotateImagesResponse;
import com.google.cloud.vision.v1.Feature;
import com.google.cloud.vision.v1.GcsDestination;
import com.google.cloud.vision.v1.Image;
import com.google.cloud.vision.v1.ImageAnnotatorClient;
import com.google.cloud.vision.v1.ImageSource;
import com.google.cloud.vision.v1.OutputConfig;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
public class AsyncBatchAnnotateImages {
public static void asyncBatchAnnotateImages()
throws InterruptedException, ExecutionException, IOException {
String inputImageUri = "gs://cloud-samples-data/vision/label/wakeupcat.jpg";
String outputUri = "gs://YOUR_BUCKET_ID/path/to/save/results/";
asyncBatchAnnotateImages(inputImageUri, outputUri);
}
public static void asyncBatchAnnotateImages(String inputImageUri, String outputUri)
throws IOException, ExecutionException, InterruptedException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (ImageAnnotatorClient imageAnnotatorClient = ImageAnnotatorClient.create()) {
// You can send multiple images to be annotated, this sample demonstrates how to do this with
// one image. If you want to use multiple images, you have to create a `AnnotateImageRequest`
// object for each image that you want annotated.
// First specify where the vision api can find the image
ImageSource source = ImageSource.newBuilder().setImageUri(inputImageUri).build();
Image image = Image.newBuilder().setSource(source).build();
// Set the type of annotation you want to perform on the image
// https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.Feature.Type
Feature feature = Feature.newBuilder().setType(Feature.Type.LABEL_DETECTION).build();
// Build the request object for that one image. Note: for additional images you have to create
// additional `AnnotateImageRequest` objects and store them in a list to be used below.
AnnotateImageRequest imageRequest =
AnnotateImageRequest.newBuilder().setImage(image).addFeatures(feature).build();
// Set where to store the results for the images that will be annotated.
GcsDestination gcsDestination = GcsDestination.newBuilder().setUri(outputUri).build();
OutputConfig outputConfig =
OutputConfig.newBuilder()
.setGcsDestination(gcsDestination)
.setBatchSize(2) // The max number of responses to output in each JSON file
.build();
// Add each `AnnotateImageRequest` object to the batch request and add the output config.
AsyncBatchAnnotateImagesRequest request =
AsyncBatchAnnotateImagesRequest.newBuilder()
.addRequests(imageRequest)
.setOutputConfig(outputConfig)
.build();
// Make the asynchronous batch request.
AsyncBatchAnnotateImagesResponse response =
imageAnnotatorClient.asyncBatchAnnotateImagesAsync(request).get();
// The output is written to GCS with the provided output_uri as prefix
String gcsOutputUri = response.getOutputConfig().getGcsDestination().getUri();
System.out.format("Output written to GCS with prefix: %s%n", gcsOutputUri);
}
}
}Node.js
이 샘플을 사용해 보기 전에 Vision 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vision Node.js API 참조 문서를 참조하세요.
Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const inputImageUri = 'gs://cloud-samples-data/vision/label/wakeupcat.jpg';
// const outputUri = 'gs://YOUR_BUCKET_ID/path/to/save/results/';
// Imports the Google Cloud client libraries
const {ImageAnnotatorClient} = require('@google-cloud/vision').v1;
// Instantiates a client
const client = new ImageAnnotatorClient();
// You can send multiple images to be annotated, this sample demonstrates how to do this with
// one image. If you want to use multiple images, you have to create a request object for each image that you want annotated.
async function asyncBatchAnnotateImages() {
// Set the type of annotation you want to perform on the image
// https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.Feature.Type
const features = [{type: 'LABEL_DETECTION'}];
// Build the image request object for that one image. Note: for additional images you have to create
// additional image request objects and store them in a list to be used below.
const imageRequest = {
image: {
source: {
imageUri: inputImageUri,
},
},
features: features,
};
// Set where to store the results for the images that will be annotated.
const outputConfig = {
gcsDestination: {
uri: outputUri,
},
batchSize: 2, // The max number of responses to output in each JSON file
};
// Add each image request object to the batch request and add the output config.
const request = {
requests: [
imageRequest, // add additional request objects here
],
outputConfig,
};
// Make the asynchronous batch request.
const [operation] = await client.asyncBatchAnnotateImages(request);
// Wait for the operation to complete
const [filesResponse] = await operation.promise();
// The output is written to GCS with the provided output_uri as prefix
const destinationUri = filesResponse.outputConfig.gcsDestination.uri;
console.log(`Output written to GCS with prefix: ${destinationUri}`);
}
asyncBatchAnnotateImages();Python
이 샘플을 사용해 보기 전에 Vision 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vision Python API 참조 문서를 참조하세요.
Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
from google.cloud import vision_v1
def sample_async_batch_annotate_images(
input_image_uri="gs://cloud-samples-data/vision/label/wakeupcat.jpg",
output_uri="gs://your-bucket/prefix/",
):
"""Perform async batch image annotation."""
client = vision_v1.ImageAnnotatorClient()
source = {"image_uri": input_image_uri}
image = {"source": source}
features = [
{"type_": vision_v1.Feature.Type.LABEL_DETECTION},
{"type_": vision_v1.Feature.Type.IMAGE_PROPERTIES},
]
# Each requests element corresponds to a single image. To annotate more
# images, create a request element for each image and add it to
# the array of requests
requests = [{"image": image, "features": features}]
gcs_destination = {"uri": output_uri}
# The max number of responses to output in each JSON file
batch_size = 2
output_config = {"gcs_destination": gcs_destination, "batch_size": batch_size}
operation = client.async_batch_annotate_images(
requests=requests, output_config=output_config
)
print("Waiting for operation to complete...")
response = operation.result(90)
# The output is written to GCS with the provided output_uri as prefix
gcs_output_uri = response.output_config.gcs_destination.uri
print(f"Output written to GCS with prefix: {gcs_output_uri}")
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